Rheumatoid Arthritis Care Experiences of Black People Living in Canada: A Qualitative Study to Inform Health Service Improvements
Bibliographic record
Abstract
OBJECTIVE: To understand experiences related to rheumatoid arthritis (RA) care and propose service-level strategies to reduce and mitigate inequities for Black people living in Canada. METHODS: Purposive and respondent driven sampling was used to recruit participants for qualitative interviews to explore population factors relevant to RA care and challenges and facilitators for access to health care services, medications, and enacting preferred treatment plans. Thematic analysis was conducted using the Braun and Clarke method with inductive and deductive coding and critical race theory guiding analysis. RESULTS: Six women and two men with RA, and two women health care professionals, expressed how their racial identity contributed to their understanding of RA, preferences for treatment, and outcome goals. Health care access was influenced by financial limitations and racism, by exclusion, and discrimination, and also by cultural norms in seeking health care and awareness about RA within the Black community. Participants experienced health system fragmentation and were not connected to ancillary supports. Treatment decision-making was influenced by the legacy of oppression and medical experimentation on Black people and the predominance of biomedical approaches emphasized by health care providers. Holistic and cultural approaches, provided in safe, trauma-informed care environments, with flexibility in service models, are desired. Partnerships between arthritis care services and Black community organizations are proposed to promote community awareness and knowledge about arthritis and provide support mechanisms for patients within their community. CONCLUSION: Our study highlights unique considerations based on race and ethnicity and provides suggestions for arthritis care to mitigate inequities for Black people living with arthritis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".